Field of Invention
The present invention relates generally to detection and location of impairments in Hybrid Fiber-Coax (HFC) networks, and more particularly to methods and systems for detecting leakage of signals from an HFC network and locating the sources of the leakage in the network.
Background Art
The task of detecting and locating signal leakage from a coaxial cable part of an HFC network is important for preventing such signal leakage (“egress”) from interfering with aeronautical and Long Term Evolution (LTE) wireless communication systems. Also, the repair of signal leaks will prevent over-the-air signals from entering and interfering with the HFC network (“ingress”). Leakage detection and location in a modern HFC network (e.g., using a CCAP architecture) presents challenges. First, migration to digital signals, such as QAM signals, has required new leakage/signal detection schemes. A QAM signal looks like noise, thus making it difficult to detect using traditional, narrowband analog leakage detectors. Another type of digital signal, introduced under the Data-Over-Cable Service Interface Specifications (DOCSIS) 3.1 specification (published by Cable Television Laboratories, Inc., Louisville, Colo.), is a wideband (up to 192 MHz) OFDM signal. The OFDM signal has a substantial noise-like component, but is not a simple haystack-shaped spectrum like a QAM signal. Thus, detection of an OFDM signal (e.g., by a sensitive spectrum analyzer) can be more complicated than detection of a QAM signal.
Another challenge in modern HFC networks concerns the proposed CCAP architecture. In a CCAP architecture, there are a number of narrowcast channels (SDV, VOD, DOCSIS, etc.) in the RF downstream spectrum. Each narrowcast channel is formed at a Cable Modem Termination System (CMTS) card and serves only a group of nodes or even a single node. There are a number of CMTS cards, each serving a different node or group of nodes and each potentially containing a different arrangement of narrowcast channels. Thus, leakage detection equipment must have information about all narrowcast channels from the different CMTS's to effectively detect and locate leakage over the entire HFC network.
One method of detecting leakage of digital signals involves injecting into the HFC network a predefined pilot or test signal modulated with specific information (i.e., “tag signal”). This method has been used for many years for detecting analog leakage signals. For example, see the following patents: U.S. Pat. No. 4,072,899 to Shimp; U.S. Pat. No. 6,018,358 to Bush; U.S. Pat. No. 6,600,515 to Bowyer et al.; and U.S. Pat. No. 6,804,826 to Bush et al. This method has also been used for digital leakage detection where an unoccupied channel or gap in the downstream spectrum is allocated for the tag or pilot (preferably near a digital channel). Examples of injecting a CW pilot or pilots among QAM signals (i.e., between QAM channels) are disclosed in: U.S. Pub. Patent App. No. 2011/0267474 (Nov. 3, 2011); PCT Pub. App. WO2013003301 (Jan. 3, 2013); U.S. Pub. Patent App. 2014/0105251 (Apr. 17, 2014); and U.S. Pat. No. 8,749,248 (Jun. 10, 2014). A disadvantage of this method is that extra signals must be injected into the network. Thus, there is a risk that the pilot signals will interfere with network signals. In the case of OFDM signals, the injection of additional pilots may impact data transmission efficiency. Also, in a CCAP architecture, the injection of pilot signals at each RF port of all CMTS cards is not a trivial task and may not even be possible. It may be especially complex or impossible in Fiber Deep systems proposed by Aurora Networks, Santa Clara, Calif. (www.aurora.com).
Another method of detecting digital leakage is based on a coherent cross-correlation method described in U.S. Pat. Nos. 8,456,530 and 8,904,460, issued to the Inventor herein. A commercial embodiment of such a method is supplied by ARCOM DIGITAL, LLC, Syracuse, N.Y., under the brand name QAM Snare®. This method is based on the steps of: (1) sampling the downstream digital signals under synchronization of a stable GPS clock; (2) transmitting those samples to a leakage detector in the field via wireless IP network; and (3) coherently cross-correlating those samples with samples of a received over-the-air leakage signal. The leakage signal is detected (under noisy conditions) from a cross-correlation peak resulting from the cross-correlation. An advantage of this method is that there is no need to inject a tag or pilot signal into the HFC network. Also, this method works with any noise-like digital signal, such as a QAM or OFDM signal. Further, this method allows one to measure the time delay of the QAM or OFDM signal (e.g., from headend to leakage detector) and use that delay to determine a location of the leak. A potential limitation of this method is that equipment for sampling the downstream signal must be installed at the headend (or other reference point in the network). Also, the method is most suited for detecting leakage of broadcast channel signals. As mentioned, the trend in modern networks is to reduce broadcast channels in favor of narrowcast channels. The use of narrowcast channels would require a number of signal sampling systems (including a wireless network capability) at the CMTS cards, which is a complex and costly requirement. Further, a continuous wireless connection between the CMTS cards and the leakage detector may be required (for transmission of downstream signal samples). This requirement is a problem in areas where wireless communication is unreliable. Since narrowcast channels are likely to displace broadcast channels in HFC networks, there is a need for another leakage detection and location solution.
A non-coherent cross-correlation method for detecting leakage of a QAM signal has been proposed in U.S. Pub. Patent App. 2013/0322569 (Dec. 5, 2013). A QAM signal is detected by detecting a spectral component of the received QAM signal, where the spectral component corresponds to a known QAM symbol rate used in the HFC network under test.
Systems for detecting OFDM signals exists in “Cognitive radio” and “Spectrum sensing” wireless communication systems. See for example: Shi et al., Improved Spectrum Sensing for OFDM Cognitive Radio in the Presence of Timing Offset, pp. 1-9, 19 Dec. 2014, EURASIP Journal on Wireless Communications and Networking, Vol. 2014, Issue 224; Tripathi, Study of Spectrum Sensing Techniques for OFDM Based Cognitive Radio, pp. 4-8, August 2014, International Journal of Technology Enhancements and Emerging Engineering Research, Vol. 2, Issue 8; Lu et al., Ten Years of Research in Spectrum Sensing and Sharing in Cognitive Radio, pp. 1-16, 31 Jan. 2012, EURASIP Journal on Wireless Communications and Networking, Vol. 2012, Issue 28; Bokharaiee et al., Blind Spectrum Sensing for OFDM-Based Cognitive Radio Systems, pp. 858-71, March 2011, IEEE Transactions on Vehicular Technology, Vol. 60, No. 3, IEEE; Akyildiz et al., Cooperative Spectrum Sensing in Cognitive Radio Networks: A Survey, pp. 40-62, 19 Dec. 2010, Physical Communication, Vol. 2011, Issue 4, Elsevier B.V.; and Yiicek et al., A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications, pp. 116-30, Feb. 2009, IEEE Communications Surveys & Tutorials, Vol. 11, No. 1, First Quarter 2009, IEEE. It is believed that these detection systems do not address the specific challenges and problems presented by HFC networks.
Once signal leakage is detected, there remains the task of locating the source of the leakage. Typically, leakage location involves patrolling an HFC network plant in a service truck equipped with a leakage detector and measuring leakage levels. Then, the location of the leak is estimated based on the leakage levels measured at different points along the patrolled route (or “drive route”). Leakage location becomes most difficult in urban areas due to multipath, which causes fading of signal levels and changes in direction of arrival of the leakage signal. Examples of such leakage location methods are found in the following patent documents: U.S. Pub. App. No. 2008/0133308 (Jun. 5, 2008) to Harris; and U.S. Pat. No. 7,360,124 to Bouchard (Apr. 15, 2008).
There are systems for locating leakage sources based on the use of triangulation and directional antennas. Examples of such systems are disclosed in the following patent documents: U.S. Pat. No. 7,945,939 (May 17, 2011); U.S. Pat. No. 7,548,201 to Eckenroth et al. (Jun. 16, 2009); U.S. Pub. App. No. 2008/0133308 (Jun. 5, 2008) to Harris; and U.S. Pat. No. 6,801,162 to Eckenroth et al. (Oct. 5, 2004). These systems are adversely impacted by multipath effects, especially in urban areas. A good reference book explaining such systems and the multipath problem is Anatoly Rembovsky et al., “Radio Monitoring, Problems, Methods, and Equipment,” Springer Science+Business Media, LLC, NY, N.Y., 2009, Chapter 8 (pp. 237-316), (http://www.springer.com/la/book/9780387980997). The use of a directional antenna is not convenient in a patrolling truck, which is probably why these systems have not been widely adopted.
The QAM Snare® system by ARCOM DIGITAL, LLC, Syracuse, N.Y., locates sources of QAM signal leakage in HFC networks. The location methods used by QAM Snare® are described in U.S. Pat. Nos. 8,456,530 and 8,904,640. The preferred method employs the Time Difference of Arrival (TDOA) algorithm, which is based on measuring time delay of the leaked signal propagating from the headend to the leakage detector. Time delay is measured by sampling an originally transmitted QAM signal at the headend and sampling the same QAM signal as a leaked signal at a leakage detector. The sampling operation is synchronized at both ends by GPS synchronized clocks. The headend samples and the leakage samples are cross-correlated to create a cross-correlation peak (as previously indicated for detection). The position of the cross-correlation peak in the cross-correlation function provides a time delay to be used in the TDOA algorithm. With at least three time delay measurements at three different detection points, the TDOA algorithm is able to yield an accurate estimate of the leak's location. This method is very robust in a multipath environment, because all reflected signals (multiple signal paths) have some time delay offset from the main cross-correlation peak (true time delay) and thus can be distinguished. However, as discussed, this method may be a challenge to implement in modern HFC networks, because OFDM narrowcast signals are unique to each node, making it difficult to capture signal samples at each CMTS card.
Although modern HFC networks present challenges for leakage detection and location, the use of OFDM signals may present some opportunities. Unlike nearly random QAM signals, OFDM signals include some stable or periodic components, such as continuous pilot subcarriers (“pilots”) and the Physical layer Link Channel (PLC) preamble (see, e.g., DOCSIS 3.1 specification, Sections 7.5.13.2 & 7.5.15.2 and FIGS. 7-55, 7-56 & 7-77). There are proposals to use OFDM pilots for detecting leakage of OFDM signals from HFC networks. See, for example: U.S. Pat. App. Pub. No. 2014/0294052 to Currivan et al. (Oct. 2, 2014) and U.S. Pat. App. Pub. No. 2015/0341810 to Murphy (Nov. 26, 2015). These proposals concern leakage detection and do not offer a leakage location solution beyond conventional GPS positioning of the leakage detector and signal level monitoring.
For the purpose of leakage detection, it would be advantageous for cable operators to assign the same frequency locations to some pilots for all narrowcast OFDM signals serving the different nodes in the HFC network. In such case, the content of the OFDM signals will remain unique at each node, consistent with the purpose of narrowcast channel programming. Also, with respect to the PLC preamble (hereinafter “PLC”), cable operators may consider locating the PLC at the same frequency location across all nodes, because the DOCSIS 3.1 specification (Section 7.5.13.2) encourages cable operators to locate the PLC in a “quiet” area of the downstream spectrum, i.e., where it would not be impacted by over-the-air interfering signals (e.g., wireless telephone communications at LTE frequencies).
The Inventor herein has recently proposed the use of the PLC for both detection and location of OFDM signal leakage in an HFC network. A system and method of doing so is disclosed and claimed in co-pending application Ser. No. 14/855,643, filed Sep. 16, 2015, naming the same inventor as herein. The system and method involves the prior creation of a signature of each OFDM signal (associated with each CMTS card and node). Each signature (in the form of samples) is a substitute for samples of the actual transmitted OFDM signal (as would be required in the prior QAM Snare® system discussed above). Thus, the use of signatures dispenses with the requirement of real-time samples to be taken at each CMTS card. The signatures are transmitted from a central server to a leakage detector patrolling the HFC network in the field. The samples of the signatures are cross-correlated with samples of received OFDM leakage signals. The cross-correlation is in synchronism with the period of transmission of the OFDM signals. This synchronized (coherent) cross-correlation produces time delay information of the leakage signal, which allows use of the TDOA algorithm for locating the leaks source.
Notwithstanding the merits of the above PLC method, the Inventor herein has considered whether another method could be devised to take advantage of the narrowband stable or periodic signal components of an OFDM signal. As a result, he has conceived the idea of using the principle of Doppler shift (a frequency shift caused by movement to and from a signal source) in a method of locating leakage sources in an HFC network. It is widely-known in navigation systems that narrowband signals (e.g., continuous wave (CW) signals) can be employed to implement a so-called Doppler method of locating signals sources. The Doppler method involves installing a narrowband radio receiver on a mobile platform (e.g., satellite, aircraft or truck). Due to the motion of the platform and receiver, the frequency of the received signal experiences a Doppler shift (a shift in frequency from its original center frequency). By measuring the Doppler shift at different points relative to an unknown signal source, an estimate can be made of the source's location. This is possible because Doppler shift varies depending on location and speed of the platform/receiver relative to the signal source. The Doppler location method is used in satellite navigation systems, such as the Cospas-Sarsat system, Montreal, Quebec, Canada (www.cospas-sarsat.int), and the Argos System (www.argos-system.org). Some articles have described successful test using the Doppler method to locate GSM (Global System for Mobile Communications or Groupe Spécial Mobile) base transceiver stations operating at 800 MHz. See, e.g., Piotr Gajewski, et al., Mobile Location Method of Radio Wave Emission Sources, Piers Online, Vol. 5, No. 5, August 2009, pp. 476-80, Military University of Technology, Poland (www.researchgate.net/publication/241687451_mobile_location_method_of radio_wave_emission_sources).
The application of the Doppler method to the field of locating OFDM leakage in an HFC network is possible, because Doppler shift can be measured from a narrowband stable or periodic signal component of an OFDM signal (e.g., a dominant harmonic of a continuous pilot subcarrier), and because Doppler shift at LTE frequencies (e.g., 700 MHz) is large enough (e.g., tens of Hertz) even at low speeds of the service truck (e.g., 20-40 km/h).
Implementation of the Doppler method to locate signal leaks in an HFC network is not without challenges. The first challenge is dealing with multipath effects in urban areas. Reflections of the original leakage signal arrive at the deployed leakage receiver from different directions. Thus, the Doppler shift will be different for each reflected signal. As a result, the spectrum of the original leakage signal will be corrupted, which may yield erroneous Doppler shift measurements. Also, the reflected signals have different phases, which cause fading of the original leakage signal (i.e., a reduction in measured signal level). Fading reduces signal-to-noise ratio (SNR). A reduced SNR will also reduce the accuracy of Doppler shift measurement. The effect of multipath on Doppler shift measurements is addressed in the above-cited Gajewski article.
A second challenge in applying the Doppler method to HFC networks is the fact that, even with using IEEE Precision Time Protocol (or PTP IEEE1588) synchronization, the accuracy of the CMTS master clocks are still limited and will drift. For example, in a DOCSIS specification, entitled “Remote DOCSIS Timing Interface,” CM-SP-R-DTI-I01-150615, p. 36, five levels of frequency synchronization performance is specified: from +/−5 PPB (Level 1 system) to +/−250 PPB (level 5 system). At LTE frequencies (e.g., 700 MHz), +/−5 PPB corresponds to +/−3.5 Hz and +/−250 PPB corresponds to +/−174 Hz. As a result, the actual frequency of the leakage signals in each CMTS serviced area will have some random offset or error from its nominal or specified value; and, the offsets are a priori unknown at the leakage detector. The range of expected Doppler shift at LTE frequencies for a truck speed of 100 km/h is only about +/−70 Hz (about 7 Hz per 10 km/h). Thus, the CMTS frequency offset could swallow-up the actual Doppler shift measurement. Without some solution, the application of the Doppler method to HFC networks is not an attractive idea.
Another challenge in applying the Doppler method to HFC networks is the potential for an ambiguity regarding the location of the leak. An ambiguity could arise in the scenario where a truck (equipped with a leakage detector) moves along a road and the leak is on one side of the road (a typical case). In such case, the Doppler shift may indicate that the leak location is on either side of road. The Doppler method, by itself, will not resolve the ambiguity.